Description
Generative AI with GCP Vertex AI Overview and Progression
- Platform summary: Vertex AI is Google Cloud’s unified platform for building, deploying, and operating generative AI solutions at scale.
- Studio and low‑code tooling: Vertex AI Studio provides a collaborative UI for prompt engineering, model exploration, and rapid prototyping.
- Managed foundation models: Access to Google’s and partner foundation models with hosted inference, tuning, and safety controls.
- Custom training and tuning: Managed training for custom models with distributed GPUs/TPUs, hyperparameter tuning, and experiment tracking.
- Model registry and serving: Versioned model registry, A/B and canary deployments, online and batch endpoints for production inference.
- Vertex Pipelines for MLOps: Reproducible pipelines that orchestrate data prep, training, evaluation, and deployment for CI/CD of models.
- Feature Store and consistent features: Centralized feature engineering and serving to ensure parity between offline training and online inference.
- Multimodal and media generation: Support for text, image, audio, and video generation and editing capabilities in enterprise previews.
- Prompting and instruction tuning: Tools for system instructions, prompt templates, and iterative prompt refinement to improve outputs.
- Agents and extensions: Agent frameworks and extension points for tool use, retrieval augmentation, and custom action execution.
- Retrieval augmented generation: Integrated vector stores, document indexing, and enterprise search to ground generative responses in customer data.
- Explainability and evaluation: Built‑in evaluation, model explainability, fairness checks, and continuous monitoring for drift and quality.
- Security and governance: IAM integration, CMEK support, private networking, and enterprise controls for data protection and compliance.
- Operational features: Autoscaling endpoints, logging, tracing, and metrics to meet SLOs and SRE practices in production.
- Cost and resource management: Options for managed vs custom compute, quota controls, and tooling to optimize training and serving spend.
- Experience progression guidance: Junior engineers (3–7 years) should master Studio, basic model ops, and RAG; mid levels (8–14 years) focus on MLOps, pipelines, and secure deployments; senior architects (15–20 years) lead governance, enterprise search/generative strategy, and cross‑team AI adoption.
